Lithium battery gradual change fault detection and evaluation method, apparatus and device, and storage medium

Abstract

The invention discloses a lithium battery gradual change fault detection and evaluation method, device and equipment and a storage medium, and the method comprises the steps: generating a corresponding differential voltage curve and a median differential voltage curve according to the terminal voltage and the median terminal voltage of each single battery in a lithium ion battery pack; for each single battery, calculating the mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, and when the mahalanobis distance is greater than the preset threshold value, judging that the single battery has a micro-short circuit fault; and acquiring a first charging voltage curve of a first discharging and charging cycle and a second charging voltage curve of a second discharging and charging cycle when two adjacent discharging and charging cycles are carried out on the single battery, calculating the micro-short-circuit current and the micro-short-circuit resistance of the single battery, and evaluating the fault degree of the lithium battery pack. According to the invention, the lithium battery gradual change fault detection precision and the fault degree evaluation efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium-ion battery fault diagnosis, and in particular to a method, device, equipment and storage medium for detecting and evaluating gradual faults of lithium batteries. Background Art

[0002] Lithium-ion batteries have the advantages of high power density, low self-discharge rate, long cycle life and no memory effect, and have been widely used in electric vehicles and grid energy storage. However, lithium-ion batteries have potential safety issues, especially accidents characterized by thermal runaway, which have caused public concern about the safety of lithium-ion batteries. In the past few years, battery pack spontaneous combustion accident analysis reports from different research institutions have shown that internal short circuit is one of the main causes of thermal runaway of lithium-ion batteries. Internal short circuit is gradual and is a gradual fault. Specifically, there is usually a long evolution and development process before the internal short circuit deteriorates into thermal runaway. In the early internal short circuit (i.e., micro short circuit), the equivalent short circuit resistance is large, and the battery electric and thermal parameters caused by the short circuit do not change significantly, which is highly concealed. This also makes it difficult to detect early micro short circuit faults. Therefore, timely diagnosis of early micro short circuit faults is a crucial and challenging task to ensure the safe and stable operation of lithium-ion batteries.

[0003] Existing diagnostic methods for micro short circuits in battery packs can be divided into two categories based on diagnostic effects: qualitative detection and quantitative diagnosis. Qualitative detection methods cannot quantitatively calculate short-circuit current and short-circuit resistance, that is, they cannot give a quantitative description of the severity of the short circuit. Existing quantitative diagnostic methods have the following limitations: (1) Existing methods require the establishment of an equivalent circuit model for each battery cell in the battery pack. The equivalent circuit model of lithium-ion batteries is difficult to reflect the internal reaction mechanism of the battery, and its accuracy is greatly affected by the model parameters, so that the accuracy of the diagnostic results is limited by the accuracy of the model itself and is easily affected by the model parameters. (2) Existing methods need to calculate the short-circuit current and short-circuit resistance of each cell in the battery pack one by one, and determine whether it has a fault based on the short-circuit current and short-circuit resistance. This method of indiscriminately calculating short-circuit resistance lacks specificity and increases the computational burden of the battery management system (BMS) for battery packs consisting of hundreds or thousands of cells. Summary of the invention

[0004] The present invention provides a method, device, equipment and storage medium for detecting and evaluating gradual failure of a lithium battery, so as to solve the technical problem that the existing method needs to establish an equivalent circuit model for each battery cell in a battery pack, calculate the short-circuit current and short-circuit resistance of each cell in the battery pack one by one, and judge whether a failure occurs according to the short-circuit current and short-circuit resistance.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a method for detecting and evaluating gradual failure of a lithium battery, comprising:

[0006] Obtaining the terminal voltage of each battery cell in the lithium-ion battery pack, and sorting the terminal voltage of each battery cell, and taking the median of the sorted terminal voltage as the median terminal voltage of the corresponding battery cell;

[0007] According to the terminal voltage, the differential voltage corresponding to each battery cell is calculated, and the corresponding differential voltage curve is generated; according to the median terminal voltage, the median differential voltage corresponding to the lithium-ion battery pack is calculated, and the corresponding median differential voltage curve is generated;

[0008] For each battery cell, calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, and comparing the Mahalanobis distance with a preset threshold value, and when the Mahalanobis distance is greater than the preset threshold value, determining that a micro short circuit fault occurs in the battery cell;

[0009] For a battery cell with a micro-short circuit failure, obtaining a first charging voltage curve of a first discharge and charge cycle and a second charging voltage curve of a second discharge and charge cycle when the battery cell is subjected to two adjacent discharge and charge cycles;

[0010] The micro short-circuit current and the micro short-circuit resistance of the battery cell are calculated according to the first charging voltage curve and the second charging voltage curve, and then the fault degree of the lithium battery pack is evaluated according to the micro short-circuit current and the micro short-circuit resistance.

[0011] As a preferred solution, before calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, the method further includes:

[0012] According to a preset moving average filter, smoothing the differential voltage curve and the median differential voltage curve;

[0013] The moving average filter is:

[0014]

[0015] Among them, s r is the value of the original signal s at the rth moment, is the corresponding filtered value, 2N p +1 is the window size of the sliding filter, N p is an integer, l is the time lag number, s r-l is the value of the original signal at the rlth moment.

[0016] As a preferred solution, the differential voltage corresponding to each battery cell or the median differential voltage corresponding to the lithium-ion battery pack is calculated by the following formula:

[0017]

[0018] Wherein, DV is the differential voltage curve corresponding to each battery cell or the median differential voltage curve corresponding to the lithium-ion battery pack, Q is the battery charging capacity, V is the terminal voltage in the constant current charging mode, and Q2-Q1 is the charging capacity change within the voltage interval ΔV.

[0019] As a preferred solution, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated by the following formula:

[0020]

[0021] D=[D(1),D(2),…,D(N)];

[0022] D med =[D med (1),D med (2),...,D med (N)];

[0023] Among them, d M (D med , D) is the Mahalanobis distance between the differential voltage curve and the median differential voltage curve, D is the differential voltage curve corresponding to the battery cell, and D med is the median differential voltage curve corresponding to the lithium-ion battery pack, N is the length of the differential voltage curve and the median differential voltage curve, μ is the mean vector of the differential voltage curve and the median differential voltage curve; Σ is the covariance matrix between the differential voltage curve and the median differential voltage curve.

[0024] As a preferred solution, the calculating of the micro short-circuit current and micro short-circuit resistance of the battery cell according to the first charging voltage curve and the second charging voltage curve includes:

[0025] According to the second charging voltage curve, obtaining a voltage at the end of a second discharge and charge cycle, and interpolating the voltage on the first charging voltage curve, and using the interpolated moment as a first moment;

[0026] taking the moment when the first discharge and charge cycle ends as the second moment, and taking the time interval between the first moment and the second moment as the remaining charging time;

[0027] The charging current data during the first discharge and charge cycle and the second discharge and charge cycle are obtained, and the corresponding remaining charging capacity is calculated according to the remaining charging time and the charging current data, and then the micro-short-circuit current and micro-short-circuit resistance of the battery cell are calculated according to the remaining charging capacity.

[0028] As a preferred solution, the remaining charging capacity is calculated using the following formula:

[0029]

[0030] Among them, Q Δ is the remaining charge capacity, Δt is the remaining charging time, and I is the charging current data during the first discharge and charge cycle and the second discharge and charge cycle.

[0031] As a preferred solution, the micro short-circuit current of the battery cell is calculated by the following formula:

[0032]

[0033] Among them, I err is the micro short-circuit current of the battery cell, T c is the time for one discharge and charge cycle;

[0034] The micro short-circuit resistance of the battery cell is calculated by the following formula:

[0035]

[0036] Among them, R err is the micro short-circuit resistance of the battery cell, and U is the average voltage of the discharge and charge cycles.

[0037] On the basis of the above-mentioned embodiment, another embodiment of the present invention provides a detection and evaluation device for gradual fault of a lithium battery, comprising: a terminal voltage acquisition module, a differential voltage calculation module, a micro-short circuit fault judgment module, a charging voltage curve acquisition module and a fault degree evaluation module;

[0038] The terminal voltage acquisition module is used to acquire the terminal voltage of each battery cell in the lithium-ion battery pack, and sort the terminal voltage of each battery cell, and use the median of the sorted terminal voltage as the median terminal voltage of the corresponding battery cell;

[0039] The differential voltage calculation module is used to calculate the differential voltage corresponding to each battery cell according to the terminal voltage and generate a corresponding differential voltage curve, and calculate the median differential voltage corresponding to the lithium-ion battery pack according to the median terminal voltage and generate a corresponding median differential voltage curve;

[0040] The micro-short circuit fault judgment module is used to calculate the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve for each battery cell, and compare the Mahalanobis distance with a preset threshold value, and when the Mahalanobis distance is greater than the preset threshold value, determine that the battery cell has a micro-short circuit fault;

[0041] The charging voltage curve acquisition module is used to acquire, for a battery cell with a micro-short circuit fault, a first charging voltage curve of a first discharge and charge cycle and a second charging voltage curve of a second discharge and charge cycle when the battery cell is subjected to two adjacent discharge and charge cycles;

[0042] The fault degree assessment module is used to calculate the micro short-circuit current and micro short-circuit resistance of the battery cell according to the first charging voltage curve and the second charging voltage curve, and then assess the fault degree of the lithium battery pack according to the micro short-circuit current and micro short-circuit resistance.

[0043] Based on the above embodiments, another embodiment of the present invention provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the method for detecting and evaluating gradual failure of a lithium battery as described in the above invention embodiments is implemented.

[0044] Based on the above embodiments, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the lithium battery gradual failure detection and evaluation method described in the above invention embodiment.

[0045] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0046] The present invention provides a method for detecting and evaluating gradual failure of a lithium battery. First, it is detected whether each battery cell in a lithium battery pack has a micro-short circuit failure: the terminal voltage of each battery cell in the lithium-ion battery pack is obtained, and the terminal voltage of each battery cell is sorted, and the median of the sorted terminal voltage is used as the median terminal voltage of the corresponding battery cell; according to the terminal voltage, the differential voltage corresponding to each battery cell is calculated, and a corresponding differential voltage curve is generated; according to the median terminal voltage, the median differential voltage corresponding to the lithium-ion battery pack is calculated, and a corresponding median differential voltage curve is generated; for each battery cell, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated, and the Mahalanobis distance is compared with a preset threshold value. When the Mahalanobis distance is greater than the preset threshold value, it is determined that a micro-short circuit failure has occurred in the battery cell.

[0047] Then, the degree of micro-short circuit failure of the battery cell is evaluated: for the battery cell with micro-short circuit failure, the first charging voltage curve of the first discharge and charging cycle and the second charging voltage curve of the second discharge and charging cycle are obtained when the battery cell is subjected to two adjacent discharge and charging cycles; based on the first charging voltage curve and the second charging voltage curve, the micro-short circuit current and the micro-short circuit resistance of the battery cell are calculated, and then the degree of failure of the lithium battery pack is evaluated based on the micro-short circuit current and the micro-short circuit resistance.

[0048] It can be seen that the method of the present invention does not need to establish a battery model in the process of detecting whether each battery cell in the lithium battery pack has a micro-short circuit fault and evaluating the degree of its micro-short circuit fault, thereby avoiding the difficulty of establishing an accurate battery model and improving the detection accuracy of lithium battery gradual faults. In addition, the method proposed by the present invention adopts the method of first detecting and then quantitatively evaluating, and only calculates the micro-short circuit current and micro-short circuit resistance for the battery cell that is determined to have a micro-short circuit fault, which makes its calculation process highly targeted, can avoid a large number of unnecessary calculations, and improve evaluation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a flow chart of a method for detecting and evaluating gradual failure of a lithium battery provided by an embodiment of the present invention;

[0050] Figure 2 It is a schematic diagram of the remaining charging capacity and the remaining charging time;

[0051] Figure 3 It is a schematic diagram for judging micro short circuit fault according to the change of remaining charging capacity;

[0052] Figure 4 It is a schematic diagram of the remaining charging time of the battery due to micro-short circuit leakage at the end of two cycles;

[0053] Figure 5 It is a schematic diagram of the Mahalanobis distance of each monomer when no micro short circuit occurs in cycle 1;

[0054] Figure 6 It is a schematic diagram of the Mahalanobis distance of each monomer when no micro short circuit occurs in cycle 2;

[0055] Figure 7 It is a schematic diagram of the Mahalanobis distance of each monomer when the short-circuit resistance is 300Ω in cycle 3;

[0056] Figure 8 is a schematic diagram of the Mahalanobis distance of each monomer when the short-circuit resistance is 200Ω in cycle 5;

[0057] Fig. 9is a schematic diagram of the Mahalanobis distance of each monomer when the short-circuit resistance is 100Ω in cycle 7;

[0058] Fig.10 It is a schematic diagram of the Mahalanobis distance of each monomer when the short-circuit resistance is 50Ω in cycle 9;

[0059] Fig.11 It is a schematic diagram of the Mahalanobis distance of each monomer when the short-circuit resistance is 10Ω in cycle 11;

[0060] Fig.12 It is a schematic diagram of the Mahalanobis distance of each monomer when the short-circuit resistance is 5Ω in cycle 13;

[0061] Fig.13 It is a structural schematic diagram of a lithium battery gradual fault detection and evaluation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0064] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0065] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0066] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0067] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0068] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0069] Embodiment 1

[0070] Please refer to Figure 1 , is a flow chart of a method for detecting and evaluating a gradual failure of a lithium battery provided by an embodiment of the present invention, comprising the following specific steps:

[0071] S1, obtaining the terminal voltage of each battery cell in the lithium-ion battery pack, and sorting the terminal voltage of each battery cell, and taking the median of the sorted terminal voltage as the median terminal voltage of the corresponding battery cell;

[0072] Specifically, the present invention proposes a method for detecting and quantitatively evaluating early internal short-circuit faults of energy storage lithium-ion battery packs based on DV analysis and Mahalanobis distance. The implementation process of the proposed method mainly includes three steps: (1) Considering that the median is not affected by the maximum and minimum values, the terminal voltages between the individual cells in the lithium-ion battery pack are sorted at any time to obtain the median terminal voltage curve, and the median differential voltage curve is extracted based on this, which is used as a reference standard to represent the state of normal cells in the battery pack; (2) The Mahalanobis distance between the differential voltage curve corresponding to each cell and the median differential voltage curve is calculated. If the Mahalanobis distance of a cell is greater than the threshold, it means that it deviates from the normal state and is judged as a micro-short-circuited cell; on the contrary, cells with a Mahalanobis distance less than or equal to the threshold are identified as normal cells; (3) For the micro-short-circuited cells detected in step 2, their short-circuit current and short-circuit resistance are calculated based on the difference in charging voltage curves between adjacent cycles to quantitatively evaluate the severity of their short-circuit faults.

[0073] It can be seen that the method of the present invention does not need to establish a battery model in the process of detecting micro-short-circuited cells in the battery pack and calculating their short-circuit resistance, thus avoiding the difficulty of establishing an accurate battery model. In addition, the method proposed in the present invention adopts a technical route of first detecting and then quantitatively evaluating, which makes the short-circuit resistance estimation process more targeted, can avoid a large number of unnecessary calculations, and improve diagnostic efficiency. The following is a detailed description of the extraction of differential voltage curves, micro-short circuit detection based on Mahalanobis distance, and short-circuit resistance estimation based on charging voltage curve changes:

[0074] 1. Extraction of differential voltage curve

[0075] (1) Obtain the terminal voltage of each battery cell in the lithium-ion battery pack, and sort the terminal voltage of each battery cell, and use the median of the sorted terminal voltage as the median terminal voltage of the corresponding battery cell.

[0076] S2. Calculate the differential voltage corresponding to each battery cell according to the terminal voltage and generate a corresponding differential voltage curve; calculate the median differential voltage corresponding to the lithium-ion battery pack according to the median terminal voltage and generate a corresponding median differential voltage curve;

[0077] Preferably, the differential voltage corresponding to each battery cell or the median differential voltage corresponding to the lithium-ion battery pack is calculated by the following formula:

[0078]

[0079] Wherein, DV is the differential voltage curve corresponding to each battery cell or the median differential voltage corresponding to the lithium-ion battery pack, Q is the battery charging capacity, V is the terminal voltage in the constant current charging mode, and Q2-Q1 is the charging capacity change within the voltage interval ΔV.

[0080] Preferably, before calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, the method further comprises: smoothing the differential voltage curve and the median differential voltage curve according to a preset moving average filter;

[0081] The moving average filter is:

[0082]

[0083] Among them, s r is the value of the original signal s at the rth moment, is the corresponding filtered value, 2N p +1 is the window size of the sliding filter, N p is an integer, l is the time lag number, s r-lis the value of the original signal at the rlth moment.

[0084] (2) As mentioned above, after obtaining the median terminal voltage between cells in the lithium-ion battery pack, it is necessary to extract the median differential voltage therefrom. In addition, it is necessary to calculate the differential voltage of each cell based on the terminal voltage of each cell. The present invention uses a numerical differentiation method to obtain the differential voltage, and the calculation formula is as follows:

[0085]

[0086] Wherein, Q represents the battery charging capacity, and V is the terminal voltage in the constant current charging mode. Formula (1) represents the calculation of DV based on an equal voltage interval ΔV, and Q2-Q1 represents the change in charging capacity within the voltage interval ΔV. The present invention calculates DV based on the EVI method. In order to fully capture the characteristics of the DV curve, ΔV is set to 1mV. The median DV curve and the DV curve of the monomer are both obtained by formula (1), that is, the calculation process of the two is the same. The only difference is that when calculating the median DV curve, the median terminal voltage is input; when calculating the DV curve of the monomer, the terminal voltage of the corresponding monomer is input.

[0087] The DV curve calculated based on the above numerical differentiation method is easily affected by measurement noise. Therefore, it is necessary to use a suitable filter to obtain a smooth DV curve. The present invention uses a moving average filter to smooth the DV curve. The moving average filter smoothes the signal by calculating the average value of the signal in a preset window. Given a time-varying signal s contaminated by noise, the moving average filter can be designed as:

[0088]

[0089] Among them, s r represents the value of the original signal s at the rth moment, is the corresponding filtered value, 2N p +1 is the window size of the sliding filter and N p is an integer, l is the time lag number, s r-l is the value of the original signal at the rlth moment.

[0090] S3, for each battery cell, calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, and comparing the Mahalanobis distance with a preset threshold value, and when the Mahalanobis distance is greater than the preset threshold value, determining that a micro short circuit fault occurs in the battery cell;

[0091] Preferably, the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated by the following formula:

[0092]

[0093] D=[D(1),D(2),…,D(N)];

[0094] D med =[D med (1),D med (2),...,D med (N)];

[0095] Among them, d M (D med , D) is the Mahalanobis distance between the differential voltage curve and the median differential voltage curve, D is the differential voltage curve corresponding to the battery cell, and D med is the median differential voltage curve corresponding to the lithium-ion battery pack, N is the length of the differential voltage curve and the median differential voltage curve, μ is the mean vector of the differential voltage curve and the median differential voltage curve; Σ is the covariance matrix between the differential voltage curve and the median differential voltage curve.

[0096] 2. Micro short circuit monomer detection based on Mahalanobis distance

[0097] After extracting the median differential voltage curve and the differential voltage curve of each cell, the present invention uses the Mahalanobis distance to measure the similarity between the differential voltage curve of each cell and the median differential voltage curve to detect the micro-short-circuited cell in the battery pack. The Mahalanobis distance not only considers the Euclidean distance between two points, but also the covariance structure between them, so it has higher sensitivity under different distributions and scales. Specifically, the present invention represents the median differential voltage curve and the differential voltage curve of a cell as D med and D:

[0098] D med =[D med (1),D med (2),...,D med (N)] (3)

[0099] D=[D(1),D(2),…,D(N)] (4)

[0100] Where N is the length of the median differential voltage curve and the single differential voltage curve. In multivariate applications, Mahalanobis distance is more interesting than Euclidean distance because the former is unitless and takes into account the correlation of variables. Assume that the median differential voltage curve D med The mean vector of the differential voltage curve D of the and monomer is μ, and the covariance matrix between them is Σ, then the sample vector D med The multivariate Mahalanobis distance relative to μ is defined as:

[0101]

[0102] D med The Mahalanobis distance between and D is defined as:

[0103]

[0104] Based on the above process, the Mahalanobis distances corresponding to all cells in the battery pack can be calculated. Cells with Mahalanobis distances greater than a threshold are detected as having a micro short circuit, and conversely, cells with Mahalanobis distances lower than the threshold are identified as normal cells.

[0105] S4. For a battery cell with a micro-short circuit fault, obtaining a first charging voltage curve of a first discharge and charging cycle and a second charging voltage curve of a second discharge and charging cycle when the battery cell is subjected to two adjacent discharge and charging cycles;

[0106] 3. Fault severity assessment based on charging voltage curve changes

[0107] After detecting a micro-short-circuited cell in a battery pack, the present invention further estimates its short-circuit current and short-circuit resistance to quantitatively evaluate the severity and evolution stage of the micro-short circuit. The short-circuit current and short-circuit resistance estimation principles are described as follows:

[0108] For the same type of lithium battery, its SOC-OCV curve is fixed and unchanged. Existing research believes that the difference between the charging voltage curve and the open circuit voltage curve of lithium-ion batteries is caused by electrode polarization. The charging voltage curve consistency hypothesis is proposed and verified, that is, for the same type of single cells with different internal resistance, SOC and capacity, their charging voltage curves can overlap through translation or stretching transformation.

[0109] Please refer to Figure 2 , which is a schematic diagram of the remaining charging capacity and the remaining charging time. In a series battery pack, when the initial SOCs of the two battery cells are different, the battery pack is continuously charged, and the charging capacities of the two are consistent, such as Figure 2 As shown, when cell 2 reaches the threshold voltage and is fully charged at time t0, the battery pack stops charging to avoid overcharging of the battery. At this time, the distance ΔQ from the fully charged cell 1 is the remaining charge capacity. The terminal voltage curve of cell 1 is translated to the curve of cell 2, and t0-Δt is the end of the translated curve of cell 1. According to the consistency of the charging voltage curve, if cell 1 is continuously charged to t0+Δt, both cells are in a fully charged state. The time difference Δt is the remaining charging time. When the charging current data is obtained, the remaining charging time can be used to calculate the remaining charging capacity.

[0110] Please refer to Figure 3, which is a schematic diagram of judging micro short circuit faults according to the change of remaining charge capacity. Based on this, existing research proposes a micro short circuit fault diagnosis method. For batteries with micro short circuit faults, some power will be lost due to short circuit leakage, such as Figure 3 As shown, by comparing whether the remaining charge of the battery that is not fully charged at the end of two discharge-charge cycles has changed, it is determined whether a micro-short circuit fault has occurred, and the difference in the remaining charging time is used to calculate the change in the remaining charge, so as to calculate the short-circuit current and short-circuit resistance. However, this method requires that at least one cell in the battery pack be charged to full charge each time, which will result in a long detection cycle under many dynamic conditions, and the micro-short circuit problem cannot be discovered in time. In addition, this method uses the voltage curve of the normal cell as a reference to calculate the remaining charging time of the micro-short-circuit cell. In other words, the remaining charging time of the micro-short-circuit cell is obtained by horizontally comparing the voltage curves of the micro-short-circuit cell and the normal cell. In layman's terms, it is the ratio of the micro-short-circuit cell to the normal cell. This lateral comparison is easily affected by the inconsistency between the cells in the battery pack, which brings a large error to the calculation result of the short-circuit resistance.

[0111] Please refer to Figure 4 , is a schematic diagram of the remaining charging time of the battery due to micro-short circuit leakage at the end of two cycles. The method adopted by the present invention estimates the short-circuit current and short-circuit resistance by the difference in the charging voltage curves between adjacent cycles. Specifically, the latter charge-discharge cycle is recorded as cycle 2, and the previous charge-discharge cycle is recorded as cycle 1. The first charging voltage curve of cycle 1 and the second charging voltage curve at the end of cycle 2 are obtained, and the first charging voltage curve of cycle 1 is compared with the second charging voltage curve at the end of cycle 2, as shown in FIG. Figure 4 shown.

[0112] S5. Calculate the micro short-circuit current and micro short-circuit resistance of the battery cell according to the first charging voltage curve and the second charging voltage curve, and then evaluate the fault degree of the lithium battery pack according to the micro short-circuit current and the micro short-circuit resistance.

[0113] Preferably, the calculating of the micro-short-circuit current and the micro-short-circuit resistance of the battery cell according to the first charging voltage curve and the second charging voltage curve includes: obtaining the voltage at the end of the second discharge and charging cycle according to the second charging voltage curve, and interpolating the voltage on the first charging voltage curve, and taking the moment obtained after the interpolation as the first moment; taking the moment at the end of the first discharge and charging cycle as the second moment, and taking the time interval between the first moment and the second moment as the remaining charging time; obtaining the charging current data during the first discharge and charging cycle and the second discharge and charging cycle, calculating the corresponding remaining charging capacity according to the remaining charging time and the charging current data, and then calculating the micro-short-circuit current and the micro-short-circuit resistance of the battery cell according to the remaining charging capacity.

[0114] Preferably, the remaining charge capacity is calculated by the following formula:

[0115]

[0116] Among them, Q Δ is the remaining charge capacity, Δt is the remaining charging time, and I is the charging current data during the first discharge and charge cycle and the second discharge and charge cycle.

[0117] Preferably, the micro short-circuit current of the battery cell is calculated by the following formula:

[0118]

[0119] Among them, I err is the micro short-circuit current of the battery cell, T c is the time for one discharge and charge cycle;

[0120] The micro short-circuit resistance of the battery cell is calculated by the following formula:

[0121]

[0122] Among them, R err is the micro short-circuit resistance of the battery cell, and U is the average voltage of the discharge and charge cycles.

[0123] Then the voltage at the end of cycle 2 is interpolated on the voltage curve of cycle 1, and the corresponding time after interpolation is recorded as t1, and the time at the end of cycle 1 is recorded as t2, and the time interval between t1 and t2 is the remaining charging time Δt. That is, at the end of cycle 2, an additional Δt time is required to reach the same state as at the end of cycle 1.

[0124] The remaining charging time and charging current data can be used to calculate the remaining charging capacity Q Δ :

[0125]

[0126] Micro short circuit current I err From formula (8), we get: c For 1 cycle time:

[0127]

[0128] After the short-circuit current is calculated, the short-circuit resistance is calculated using the average voltage U of the charge and discharge voltage:

[0129]

[0130] After calculating the short-circuit resistance and short-circuit current, they can be directly used as scientific indicators to judge the severity of the short-circuit fault. The larger the short-circuit current, the smaller the short-circuit resistance, the more serious the short-circuit fault, and the higher the possibility of thermal runaway of the lithium battery.

[0131] The method proposed in the present invention is different from the method mentioned in the previous section in two aspects:

[0132] (1) Instead of fully charging as the end of a charge-discharge cycle, the period during which the SOC change value calculated by the ampere-hour integration method is 0 in a discharge-charge cycle is used as a micro-short circuit fault judgment period. During this period, a normal battery has no leakage, so the ampere-hour integration method is accurate, and the voltage at the end of the cycle is equal to that at the beginning; however, a battery with a micro-short circuit fault will have a drop in voltage at the end of the cycle compared to the beginning due to short circuit leakage. In this way, multiple tests can be performed under multiple discharge-charge conditions, shortening the judgment cycle, and diagnosis can still be performed under conditions where full charging is not available for a long time, and micro-short circuit changes can be monitored more frequently.

[0133] (2) Instead of calculating the remaining charge capacity of the single cell until it is fully charged, the voltage value of the single cell at the beginning of each discharge-charge cycle is used as a reference. At the end of the cycle, the remaining charging time from the reference voltage is calculated to obtain the remaining charge capacity. This part of the charge is the leakage due to the short circuit fault in the battery. The short circuit current and short circuit resistance are then calculated. This method does not rely on battery consistency and does not require the charging voltage curve to be shifted or scaled due to different consistency, thus simplifying the algorithm steps. More importantly, the advantage of doing so is that it can avoid the interference of the inherent inconsistency between cells in the battery pack and improve the accuracy of short circuit resistance estimation.

[0134] In a specific embodiment, the present invention is tested on a battery pack consisting of 8 cylindrical lithium-ion battery cells connected in series to verify the effectiveness of the above method. The cell specification parameters are shown in Table 1.

[0135]

[0136] Table 1 Specifications of lithium-ion batteries

[0137] The battery pack was charged at a constant current of 0.5C. When the maximum cell terminal voltage reached the charge cut-off voltage of 4.2V, the battery pack stopped charging to prevent the cell from overcharging. Then the battery pack was discharged for dynamic stress test. When the minimum cell terminal voltage reached the discharge cut-off voltage of 2.75V, the battery pack stopped discharging to prevent the cell from over-discharging. A total of 14 charge and discharge cycles were performed on the battery pack.

[0138] The present invention simulates micro short circuit faults by connecting external resistors in parallel at both ends of the battery. This method can not only better control the triggering time and position of the micro short circuit, but also simulate the evolution process of the micro short circuit, and has good controllability and repeatability.

[0139] Specifically, in order to simulate the evolution of micro-short circuits, short-circuit resistors of different magnitudes and corresponding manual switches are connected in series and then connected in parallel to both ends of cell 4 and cell 8. By closing different switches, short-circuit resistors of different magnitudes can be connected in parallel to both ends of the battery to simulate the occurrence and evolution of micro-short circuits. The smaller the short-circuit resistance, the larger the short-circuit current, indicating that the short circuit is more serious. The parallel short-circuit resistors corresponding to each charge and discharge cycle are shown in Table 2:

[0140] Table 2 Short-circuit resistance corresponding to different charge and discharge cycles

[0141] cycle Short-circuit resistance of Cell 4 Short-circuit resistance of Cell 8 1,2 ∞ ∞ 3,4 300Ω 300Ω 5,6 200Ω 200Ω 7,8 100Ω 100Ω 9,10 50Ω 50Ω 11,12 10Ω 10Ω 13,14 5Ω 5Ω

[0142] Table 2 Short-circuit resistance corresponding to different charge and discharge cycles

[0143] Next, the Mahalanobis distance between the DV curve of each cell and the median DV curve is calculated, and compared with a predetermined threshold to detect micro-short-circuited cells. Specifically, for the battery pack used in the present invention, the fault detection threshold is set to 0.10. It should be noted that in actual use, the fault detection threshold can be flexibly adjusted according to the battery type, application scenario, etc. to improve the sensitivity of the fault detection algorithm.

[0144] Please refer to Figure 5-Figure 12, which are schematic diagrams of the Mahalanobis distance of each monomer when no micro short circuit occurs in cycle 1, the Mahalanobis distance of each monomer when no micro short circuit occurs in cycle 2, the Mahalanobis distance of each monomer when the short circuit resistance is 300Ω in cycle 3, the Mahalanobis distance of each monomer when the short circuit resistance is 200Ω in cycle 5, the Mahalanobis distance of each monomer when the short circuit resistance is 100Ω in cycle 7, the Mahalanobis distance of each monomer when the short circuit resistance is 50Ω in cycle 9, the Mahalanobis distance of each monomer when the short circuit resistance is 10Ω in cycle 11, and the Mahalanobis distance of each monomer when the short circuit resistance is 5Ω in cycle 13. The Mahalanobis distances of each monomer in the battery pack at different charge and discharge cycles are shown in FIG. Figures 5 to 12 As shown, the dotted line represents the threshold. Figures 5 to 12 It can be observed that in the first two cycles, the Mahalanobis distances of all monomers are lower than the threshold, which indicates that all monomers are in a normal state and no micro-short circuit occurs in the first two cycles. Starting from the third cycle, the Mahalanobis distances corresponding to monomers 4 and 8 exceed the threshold. On the contrary, the Mahalanobis distances of other monomers are always lower than the threshold, which indicates that from the third cycle, monomers 4 and 8 have micro-short circuits, while other monomers are still in a normal state. In addition, it can be observed that as the short-circuit resistance decreases, the Mahalanobis distances of monomers 4 and 8 show an increasing trend, which indicates that as the micro-short circuit worsens, the Mahalanobis distances of micro-short-circuited monomers will gradually increase. The above diagnostic results are consistent with the aforementioned fault injection conditions, which shows that the proposed method can accurately detect micro-short-circuited monomers in lithium-ion battery packs, proving the effectiveness of the proposed method. Observing the fault detection results obtained from the 4th, 6th, 8th, 10th, 12th and 14th charge and discharge cycles, the same conclusion can be drawn. For simplicity, this article will not repeat them.

[0145] After detecting the micro-short-circuited cells in the battery pack, the short-circuit resistance is further estimated using the method proposed above to quantify the severity and evolution stage of the micro-short circuit. The true value, estimated value and relative error of the short-circuit resistance of micro-short-circuited cells 4 and 8 are listed in Table 3. As shown in Table 3, for micro-short-circuited cells 4, the maximum relative error of the short-circuit resistance estimation result is 5.21%, and for micro-short-circuited cells 8, the maximum relative error of the short-circuit resistance estimation result is 4.89%. In addition, it can be found from Table 3 that as the actual short-circuit resistance connected in parallel at both ends of the battery decreases, the relative error of the estimation result shows a decreasing trend as a whole. This is because as the short-circuit resistance decreases, the short-circuit current gradually increases, and the fault characteristics caused by the micro-short circuit will become more and more obvious. The above-mentioned short-circuit resistance estimation error is within an acceptable range, which proves the effectiveness of the micro-short circuit quantitative evaluation method proposed in the present invention.

[0146]

[0147]

[0148] Table 3 Micro short circuit resistance estimation results

[0149] Embodiment 2

[0150] Please refer to Fig.13 , is a schematic diagram of the structure of a lithium battery gradual fault detection and evaluation device provided by an embodiment of the present invention, the device comprising: a terminal voltage acquisition module, a differential voltage calculation module, a micro-short circuit fault judgment module, a charging voltage curve acquisition module and a fault degree evaluation module;

[0151] The terminal voltage acquisition module is used to acquire the terminal voltage of each battery cell in the lithium-ion battery pack, and sort the terminal voltage of each battery cell, and use the median of the sorted terminal voltage as the median terminal voltage of the corresponding battery cell;

[0152] The differential voltage calculation module is used to calculate the differential voltage corresponding to each battery cell according to the terminal voltage and generate a corresponding differential voltage curve, and calculate the median differential voltage corresponding to the lithium-ion battery pack according to the median terminal voltage and generate a corresponding median differential voltage curve;

[0153] The micro-short circuit fault judgment module is used to calculate the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve for each battery cell, and compare the Mahalanobis distance with a preset threshold value, and when the Mahalanobis distance is greater than the preset threshold value, determine that the battery cell has a micro-short circuit fault;

[0154] The charging voltage curve acquisition module is used to acquire, for a battery cell with a micro-short circuit fault, a first charging voltage curve of a first discharge and charge cycle and a second charging voltage curve of a second discharge and charge cycle when the battery cell is subjected to two adjacent discharge and charge cycles;

[0155] The fault degree assessment module is used to calculate the micro short-circuit current and micro short-circuit resistance of the battery cell according to the first charging voltage curve and the second charging voltage curve, and then assess the fault degree of the lithium battery pack according to the micro short-circuit current and micro short-circuit resistance.

[0156] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0157] Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0158] Embodiment 3

[0159] Accordingly, an embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for detecting and evaluating gradual failure of a lithium battery as described in the above-mentioned embodiment of the invention is implemented.

[0160] The electronic device may be a computing device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0161] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, and various interfaces and lines are used to connect various parts of the entire device.

[0162] Embodiment 4

[0163] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for detecting and evaluating gradual failure of a lithium battery as described in the above-mentioned embodiment of the invention.

[0164] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0165] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each method embodiment described above can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0166] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for detecting and evaluating gradual failure of a lithium battery, characterized in that: include: Obtaining the terminal voltage of each battery cell in the lithium-ion battery pack, and sorting the terminal voltage of each battery cell, and taking the median of the sorted terminal voltage as the median terminal voltage of the corresponding battery cell; According to the terminal voltage, the differential voltage corresponding to each battery cell is calculated, and the corresponding differential voltage curve is generated; according to the median terminal voltage, the median differential voltage corresponding to the lithium-ion battery pack is calculated, and the corresponding median differential voltage curve is generated; For each battery cell, calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, and comparing the Mahalanobis distance with a preset threshold value, and when the Mahalanobis distance is greater than the preset threshold value, determining that a micro short circuit fault occurs in the battery cell; For a battery cell with a micro-short circuit failure, obtaining a first charging voltage curve of a first discharge and charge cycle and a second charging voltage curve of a second discharge and charge cycle when the battery cell is subjected to two adjacent discharge and charge cycles; The micro short-circuit current and the micro short-circuit resistance of the battery cell are calculated according to the first charging voltage curve and the second charging voltage curve, and then the fault degree of the lithium battery pack is evaluated according to the micro short-circuit current and the micro short-circuit resistance.

2. The method for detecting and evaluating gradual failure of a lithium battery as claimed in claim 1, characterized in that: Before calculating the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve, the method further includes: According to a preset moving average filter, smoothing the differential voltage curve and the median differential voltage curve; The moving average filter is: Among them, s r is the value of the original signal s at the rth moment, is the corresponding filtered value, 2N p +1 is the window size of the sliding filter, N p is an integer, l is the time lag number, s r-l is the value of the original signal at the rlth moment.

3. The method for detecting and evaluating gradual failure of a lithium battery as claimed in claim 1, characterized in that: The differential voltage corresponding to each battery cell or the median differential voltage corresponding to the lithium-ion battery pack is calculated using the following formula: Wherein, DV is the differential voltage curve corresponding to each battery cell or the median differential voltage curve corresponding to the lithium-ion battery pack, Q is the battery charging capacity, V is the terminal voltage in the constant current charging mode, and Q2-Q1 is the charging capacity change within the voltage interval ΔV.

4. The method for detecting and evaluating gradual failure of a lithium battery as claimed in claim 1, characterized in that: The Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve is calculated by the following formula: D=[D(1),D(2),...,D(N)]; D med =[D med (1),D med (2),...,D med (N)]; Among them, d M (D med , D) is the Mahalanobis distance between the differential voltage curve and the median differential voltage curve, D is the differential voltage curve corresponding to the battery cell, and D med is the median differential voltage curve corresponding to the lithium-ion battery pack, N is the length of the differential voltage curve and the median differential voltage curve, μ is the mean vector of the differential voltage curve and the median differential voltage curve; Σ is the covariance matrix between the differential voltage curve and the median differential voltage curve.

5. The method for detecting and evaluating gradual failure of a lithium battery as claimed in claim 1, characterized in that: The calculating the micro short circuit current and micro short circuit resistance of the battery cell according to the first charging voltage curve and the second charging voltage curve includes: According to the second charging voltage curve, obtaining a voltage at the end of a second discharge and charge cycle, and interpolating the voltage on the first charging voltage curve, and using the interpolated moment as a first moment; taking the moment when the first discharge and charge cycle ends as the second moment, and taking the time interval between the first moment and the second moment as the remaining charging time; The charging current data during the first discharge and charge cycle and the second discharge and charge cycle are obtained, and the corresponding remaining charging capacity is calculated according to the remaining charging time and the charging current data, and then the micro-short-circuit current and micro-short-circuit resistance of the battery cell are calculated according to the remaining charging capacity.

6. The method for detecting and evaluating gradual failure of a lithium battery as claimed in claim 5, characterized in that: Calculate the remaining charge capacity using the following formula: Among them, Q Δ is the remaining charge capacity, Δt is the remaining charging time, and I is the charging current data during the first discharge and charge cycle and the second discharge and charge cycle.

7. The method for detecting and evaluating gradual failure of a lithium battery as claimed in claim 6, characterized in that: The micro short-circuit current of the battery cell is calculated by the following formula: Among them, I err is the micro short-circuit current of the battery cell, T c is the time for one discharge and charge cycle; The micro short-circuit resistance of the battery cell is calculated by the following formula: Among them, R em is the micro short-circuit resistance of the battery cell, and U is the average voltage of the discharge and charge cycles.

8. A lithium battery gradual fault detection and evaluation device, characterized in that: include: Terminal voltage acquisition module, differential voltage calculation module, micro-short circuit fault judgment module, charging voltage curve acquisition module and fault degree assessment module; The terminal voltage acquisition module is used to acquire the terminal voltage of each battery cell in the lithium-ion battery pack, and sort the terminal voltage of each battery cell, and use the median of the sorted terminal voltage as the median terminal voltage of the corresponding battery cell; The differential voltage calculation module is used to calculate the differential voltage corresponding to each battery cell according to the terminal voltage and generate a corresponding differential voltage curve, and calculate the median differential voltage corresponding to the lithium-ion battery pack according to the median terminal voltage and generate a corresponding median differential voltage curve; The micro-short circuit fault judgment module is used to calculate the Mahalanobis distance between the corresponding differential voltage curve and the median differential voltage curve for each battery cell, and compare the Mahalanobis distance with a preset threshold value, and when the Mahalanobis distance is greater than the preset threshold value, determine that the battery cell has a micro-short circuit fault; The charging voltage curve acquisition module is used to acquire, for a battery cell with a micro-short circuit fault, a first charging voltage curve of a first discharge and charge cycle and a second charging voltage curve of a second discharge and charge cycle when the battery cell is subjected to two adjacent discharge and charge cycles; The fault degree assessment module is used to calculate the micro short-circuit current and micro short-circuit resistance of the battery cell according to the first charging voltage curve and the second charging voltage curve, and then assess the fault degree of the lithium battery pack according to the micro short-circuit current and micro short-circuit resistance.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the method for detecting and evaluating gradual failure of a lithium battery as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the method for detecting and evaluating gradual failure of a lithium battery as described in any one of claims 1 to 7.

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